Genetic identity and Kinship
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Transcript of Genetic identity and Kinship
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Genetic identity and Kinship
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Genetic Identity
G1 and G2 are identical by descent (i.b.d) if they are physical copies of the same ancestor, or one of the other.
G1 and G2 are identical by state (i.b.s) if they represent the same allele.
The kinship between two relatives fij is the probability that random gene from autosomal loci in I and j are i.b.d.
The interbreeding coefficient is the probability that his or her two genes from autosomal loci are i.b.d.
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•Every mutation creates a new allele•Identity in state = identity by descent (IBD)
A1A1
A2A2
A1A2A1A1
A1A2 A1A2
Mutation occurred once
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A1A1
A2A2
A1A2A1A1
A1A2 A1A2
A1A1
A2A2
A1A2 A1A1
A1A2A1A2
A2 A2 IBD A2 A2 IBD
A2 A2 alike in state (AIS)
not identical by descent
The same mutation arises independently
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Identity by descent
A - B C - D | | P - Q | X
Let fAC be the coancestry of A with C etc., i.e. the probability of 2 gametes taken at random, 1 from A and one from B, being IBD.
Probability of taking two gametes, 1 from P and one from Q, as IBD, FX
FX fPQ 1
4fAD
1
4fAC
1
4fBC
1
4fBD
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Identity by descent
Example, imagine a full-sib matingA - B / \P - Q | X
Indv. X has 2 alleles, what is the probability of IBD?
FX fPQ 1
4fAD
1
4fAC
1
4fBC
1
4fBD
1
42 fAB fAA fBB 1
40 1
2 1
2
1
4
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Identity by descent
Example, imagine a half-sib matingA - B - C | | P - Q | X
FX fPQ 1
4fAD
1
4fAC
1
4fBC
1
4fBD
1
42 fAB fAC fBC fBB 1
40 0 0
1
2
1
8
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Kinship and Interbreeding
ii=0.5(1+fi) fi=kl, where k and l are the parents of i. If fi>0 then i is said to be inbred. The question is how to compute kinship,
given a pedigree. Let us look for example at brothers and
sisters:
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Pedigree
Let us compute the kinship coefficients for all member of this pedigree. We assume that 1 and 2 are not inbred and unrelated, we start counting from the oldest generation.
We will develop an algorithm to compute ij for all members of this pedigree.
1 2
3 4
5 6
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Kinship coefficient algorithm
1 2
3 4
5 6
123456
11/20
201/2
3
4
5
6
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Kinship coefficient algorithm II
1 2
3 4
5 6
123456
11/20
201/2
3
4
5
6
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Kinship coefficient algorithm III
1 2
3 4
5 6
123456
11/20
201/2
31/2
41/2
5
6
If i originates from k and l
ii= ½+ kl
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Kinship coefficient algorithm III
1 2
3 4
5 6
123456
1½0¼¼
20½¼¼
3¼¼½¼
4¼¼¼½
5
6
If i originates from k and lij= ji
= ½(jk + jl)
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Kinship coefficient algorithm IV
1 2
3 4
5 6
123456
1½0¼¼
20½¼¼
3¼¼½¼
4¼¼¼½
5
6
One can now reapply the algorithm on the next generation
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Kinship coefficient algorithm V
1 2
3 4
5 6
123456
1½0¼¼¼¼
20½¼¼¼¼
3¼¼½¼3/83/8
4¼¼¼½3/83/8
5¼¼3/83/85/83/8
6¼¼3/83/83/85/8
The final result is:
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Identity coefficients:
We can now summarize the kinship coefficient of some basic family relations:
Relation
Parent-Offspring¼
Half Sibling1/8
Full Sibling¼
First Cousins1/16
Double First Cousins1/8
Second Cousins1/64
Uncle-Nephew1/8
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Detailed Identity States – I
I
J
Allele 1 Allele 2
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Detailed Identity States – I I
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Detailed Identity States – I I I
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Summary
Many of these relations are redundant. If I is not inbred 1,2,3 and 4 will be zero.
One can define kinship in a condensed mater, if we can interchange the maternal and paternal genes.
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Condensed Identity States
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Condensed Identity States
S3=S*2S*
2
S5=S*4S*
5
S7=S*9S*
12
S8=S*10S*
11
S*13S*
14
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Condensed Identity States II
1234 are 0, when i is not inbred.
1256 are 0, when j is not inbred.
1357 and 8 are 0, when i and j are unrelated.
ji=1+1/2(357)+1/4 8
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Kinship and identity coefficients
Relation
Parent-Offspring010¼
Half Sibling0½½1/8
Full Sibling1/4½¼¼
First Cousins0¼¾1/16
Double First Cousins1/166/169/161/8
Second Cousins01/1615/161/64
Uncle-Nephew0½½1/8
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Genotype prediction.
What is the probability that i has a given genotype, given the genotype of j ?
For example, If my uncle has a genetic disease, what is the probability that I will also have it?
What are the probabilities of brothers from inbred parents to be homozygous for a disease causing gene?
……
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Genotype prediction.
4 )1(
4 0
4 2)1(
24 0
)/Pr(
)/,Pr()/|Pr(
)/|Pr(*
)/,|/Pr()/|/Pr(9
1
rf
r
rppf
ppr
lki
lkiSlkiS
lkiS
lkiSnmjlkinmj
i
r
lki
lkrr
r
r
rr
If I is heterozygous, with an inbreeding coefficient fi
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Genotype prediction II
4 )1(
4 )1(
4 )1(
4 )1(
)/Pr(
)/,Pr()/|Pr(
2
2
2
2
rpff
p
rpff
rpfpf
p
rpfpf
p
kki
kkiSkkiS
kii
kr
kii
r
kiki
kr
kiki
kr
rr
If I is homozygous, with an inbreeding coefficient fi
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Genotype prediction III
l/l k/kj
ij
ijS
ijS
lkiSnmj r
or either is 5S
with gene one shares 3,8S
oft independen is 9,6,4,2
7,1
)/,|/Pr(
When j is independent of i, it only follows the H,W equilibrium.When j is equivalent to i, the probability is one if m/n=k/l and zero otherwise.When j shares one allele with I, m/n and k/l must overlap with one allele and the other one has H.W distribution.
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Example
What is the blood type of non-inbred siblings?
BABA pppp
BAiSBAjBAiSBAj
BAiSBAjBAiBAj
24
1)
2
1
2
1(
2
11*
4
1
)/,|/Pr(4
1)/,|/Pr(
2
1
)/,|/Pr(4
1)/|/Pr(
98
7
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Example I
What is the blood type of non-inbred siblings?
oAA pppp
OOiSOAjOOiSOAj
OOiSOAj
OOiAAj
OOiOAjOOiAj
A2
4
1
2
12
4
10*
2
10*
4
1
.......................
)/,|/Pr(4
1)/,|/Pr(
2
1
)/,|/Pr(4
1
)/|/Pr(
)/|/Pr()/|Pr(
2
98
7
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Risk Ratios and Genetic Model Discrimination.
Let us assume that each person in the population is assigned a factor of X=1 if he/she is affected by a condition and X=0 otherwise.
The Prevalence of the condition is K=E(X). Given two non-inbred relatives i and j and given
that i is affected, what is the probability that J is affected?
KR=P(Xj=1|Xi=1( P(Xj=1,Xi=1) = P(Xj=1|Xi=1(P(Xi=1) = KRK =
E(XiXj)
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Risk Ratios and Genetic Model Discrimination.
P(Xj=1|Xi=1) = E(XiXj)/K = (cov(Xi,Xj)+K2)/K = cov(Xi,Xj)/K+K
This result simply represents the fact that the extra risk for j results from the covariance of X between i and j.
The risk ratio can thus be defined as: R= cov(Xi,Xj)/K2
Let us compute this covariance, and following it the risk ratio.
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Covariance
In a more general way, let us assume that i and j are non-inbred relatives. The covariance between their genes is defined only by condensed identity states 7,8 and 9
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Covariance
Let us assume that a given property is defined by a single gene with multiple alleles.
For the sake of simplicity let us normalize E(x)=0, and divide:
lk l
kkl ppXE )(
0
0 ;
kkkl
kkkkllkkl
p
p
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Covariance
27
2
27
287
28
227
9
82
7
2
24
1
2
12
22
)()(
)()()(
),|,()(
dijaij
lk l
kklijkk
kijij
kk
kijlk l
kklkk
kij
nmlk l
kmnnmm n
kllkij
mlk l
kkmmkm
kllkijlk l
kkllkij
lk l
km
kln
mnji
ppp
pppp
pppp
ppppp
pplknmpXXE
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Risk Ratio
RRelative TypeRisk Ratio
MIdentical Twina2/K2+ d
2/K2
SSiblinga2/2K2+ d
2/4K2
1First Degreea2/2K2
2Second Degreea2/4K2
3Third Degreea2/8K2
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Summary
ConceptsKinship coefficient.
Inbreeding coefficientIdentity states
Condensed identity statesIdentity coefficientsGenotype prediction